Human activity recognition (HAR) plays an important role in ubiquitous computing. Through collected raw signals from sensors, it can easily learn high-level knowledge about human activity. HAR has wide applications in many areas such as gait analysis [1]}, gesture recognition [2]} and sleep stage detection [3]}. The success of HAR is dependent on an accurate and robust machine learning model. However, to build such good models, we always need to acquire sufficient labeled training data which is time-consuming and expensive. To build models for a new activity dataset that has extremely few or even no labels, a promising approach is to transfer the knowledge learned on the labeled activity data from an auxiliary dataset that is similar to this target dataset. The new problem is referred to as the cross-domain activity recognition (CDAR) [4]} since we aim to build models for the target domain (dataset) by leveraging knowledge from the source domain (auxiliary dataset).
